2015
DOI: 10.1039/c5cp02177d
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Smooth heuristic optimization on a complex chemical subspace

Abstract: Several algorithms for optimizing a combinatorial subspace of chemical compound space with constraints are compared. The test system is a library of organic chromophores for electro-optic applications. The constraints on the optimization include the maximization of the candidate structure hyperpolarizability while keeping the absorption within acceptable limits in the range of 400-700 nm. The best pay-off in terms of primary objective, feasibility and computational cost is achieved using a heuristic reordering… Show more

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Cited by 11 publications
(13 citation statements)
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“…Convergence is reached when a single site modification does not result anymore in an improved property value. Even though there exist more recently introduced inverse design methods that may outperform the BFS method based on efficacy or larger CCS exploration ( Elward and Rinderspacher, 2015 ; Strasser et al, 2016 ; Häse et al, 2018 ), BFS has the key advantage of systematically delivering essential information on how the type and location of each possible substituent influences our property of interest, since in every global iteration all substituents are tested on all available sites ( Teunissen et al, 2017 ).…”
Section: Theoretical Background: Inverse Molecular Designmentioning
confidence: 99%
“…Convergence is reached when a single site modification does not result anymore in an improved property value. Even though there exist more recently introduced inverse design methods that may outperform the BFS method based on efficacy or larger CCS exploration ( Elward and Rinderspacher, 2015 ; Strasser et al, 2016 ; Häse et al, 2018 ), BFS has the key advantage of systematically delivering essential information on how the type and location of each possible substituent influences our property of interest, since in every global iteration all substituents are tested on all available sites ( Teunissen et al, 2017 ).…”
Section: Theoretical Background: Inverse Molecular Designmentioning
confidence: 99%
“…In the following, we quickly recapitulate the working equations used for the HNNS , and introduce EGO for combinatorial optimization in CCS. We furthermore introduce and discuss the consequences of reordering in the context of optimization complexity on HNNS and EGO as well as their theoretical justifications.…”
Section: Background and Theorymentioning
confidence: 99%
“…Consequently, a number of methods have been developed to explore chemical subspaces efficiently. ,,, Lack of structure may express itself in property roughness or constraint-induced feasibility islands. Prominent approaches to address these as well as search space discreteness are stochastic methods, ,,, in particular genetic algorithms (GAs). ,, GAs address the problem of property roughness by using populations of candidates of which not all members are solely evolved by performance.…”
Section: Introductionmentioning
confidence: 99%
“…There were some systematic studies concentrating on researching the substitutions in the p-framework from theoretical and practical domains. [23][24][25][26][27] Jen's group introduced an alkylthiol group into the electron-bridge of the CLD-type chromophore, which achieved excellent photostability and large optical nonlinearity. Dalton's group synthesized a series of chromophores based on bis-(4-methoxyphenyl)hetero-aryl-amino type donors.…”
Section: Introductionmentioning
confidence: 99%
“…Introducing an additional amino group into the donor moiety could effectively modulate the conjugation of the p-electron networks and the electronic character of the charge transfer kinetics, which was proved by simulated calculations. 11,23,[27][28][29] As classical theories, Dewar's rules 30 have been widely applied into chromophore structure design for increasing nonlinear optical activity and stability. 31,32 On the basis of this theory, a donor-bridge-acceptor push-pull type nonlinear optical chromophore shows alternating electronegativities along the charge-transfer direction (Fig.…”
Section: Introductionmentioning
confidence: 99%